The above insights can be employed as guidance to design powerful quantum kernels in the NISQ era. In particular, the size of the training dataset \(n\)  should be controlled to be small. A possible solution is constructing a coreset [1]}. In addition, the minimum number of measurements \(m\)  is required to be set as \(n^3\)  to peruse potential quantum advantages. Last, since \(p\)  is determined by the circuit depth \(L_Q\)  and the depolarization rate \(\widetilde{p}\)  as shown in Eqn. (REF ), a good quantum kernel prefers a shallow circuit depth in \(U_E\)  and advanced error mitigation techniques [2]}, [3]}, [4]}, [5]}, [6]}, [7]}.
